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tags:
- ocr
- document-processing
- glm-ocr
- markdown
- uv-script
- generated
- hf-jobs
---
# Document OCR using GLM-OCR
This dataset contains OCR results from images in [divinity-library/thg-test-01](https://huggingface.co/datasets/divinity-library/thg-test-01) using GLM-OCR, a compact 0.9B OCR model achieving SOTA performance.
## Processing Details
- **Source Dataset**: [divinity-library/thg-test-01](https://huggingface.co/datasets/divinity-library/thg-test-01)
- **Model**: [zai-org/GLM-OCR](https://huggingface.co/zai-org/GLM-OCR)
- **Task**: text recognition
- **Number of Samples**: 6
- **Processing Time**: 1.9 min
- **Processing Date**: 2026-10-28 02:37 UTC
### Configuration
- **Image Column**: `image`
- **Output Column**: `markdown`
- **Dataset Split**: `train`
- **Batch Size**: 16
- **Max Model Length**: 8,192 tokens
- **Max Output Tokens**: 8,192
- **Temperature**: 0.01
- **Top P**: 1e-05
- **GPU Memory Utilization**: 80.0%
## Model Information
GLM-OCR is a compact, high-performance OCR model:
- 0.9B parameters
- 94.62% on OmniDocBench V1.5
- CogViT visual encoder + GLM-0.5B language decoder
- Multi-Token Prediction (MTP) loss for efficiency
- Multilingual: zh, en, fr, es, ru, de, ja, ko
- MIT licensed
## Dataset Structure
The dataset contains all original columns plus:
- `markdown`: The extracted text in markdown format
- `inference_info`: JSON list tracking all OCR models applied to this dataset
## Reproduction
Produced on [Hugging Face Jobs](https://huggingface.co/docs/huggingface_hub/guides/jobs) (`a10g-small`) with the [`glm-ocr.py`](https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py) recipe from [uv-scripts](https://huggingface.co/uv-scripts). Run it yourself:
```bash
hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
divinity-library/thg-test-01 \
<output-dataset> \
--image-column image \
--batch-size 16 \
--task ocr
```
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